Machine Learning-Based Intelligent Energy Management System for Smart Grid Using Renewable Energy Sources

Abstract

As solar and wind power are increasingly integrated into modern grids, intermittency and forecasting uncertainty pose a danger to system stability. The Machine Learning-Based Intelligent Energy Management System (ML-IEMS) proposed in this paper combines a hybrid CNN-LSTM model for short-term load and renewable generation forecasting with a Reinforcement Learning (RL) dispatch agent for real-time storage, demand response, and grid exchange scheduling. A five-layer architecture, a mathematical formulation of the power balance and cost objective, a Markov Decision Process (MDP) dispatch formulation, a thorough dataset, preprocessing, and evaluation protocol, as well as template result tables and comparative charts to facilitate empirical validation, are all provided. In comparison to statistical and rule-based baselines, the framework is anticipated to boost renewable usage, lower operational costs, and improve forecasting accuracy.